Title: Numerical Methods for Large-Scale Ill-Posed Inverse Problems

Speaker: Julianne Chung

Date/Time: Monday, February 2, 2009, 9:30 – 10:30 am

Location: CSRI Building/Room 90 (VC to 915/S145 CA)

Brief Abstract: Ill-posed inverse problems arise in a variety of scientific applications. Regularization methods exist for computing stable solution approximations, but many are inadequate or insufficient for large-scale problems. This work addresses these limitations by developing advanced numerical methods to solve ill-posed inverse problems and by implementing high-performance parallel code for large-scale applications. Hybrid methods are developed for regularization of linear and nonlinear least squares problems, and an efficient parallel implementation based on the Message Passing Interface (MPI) library is described for use on state-of-the-art computer architectures. Regularization for nonlinear Poisson based models, such as those arising from digital tomosynthesis reconstruction, is significantly more challenging. However, reconstruction algorithms for polyenergetic tomosynthesis will be discussed, and numerical experiments illustrate the effectiveness and efficiency of the proposed methods.

CSRI POC: Eric Phipps, (505) 284-9268